Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(googlesheets4) # for reading googlesheet data
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
gs4_deauth()           # To not have to authorize each time you knit.
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Put your homework on GitHub!

If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 4th weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab under Stage and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

world <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "terrain",
    zoom = 2)
ggmap(world) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude,
                 color = `Ownership Type`),
             size = .1,
             alpha = .3)+
  theme_map() +
  labs(title = "Global Starbucks Locations")

In the U.S and most of Europe, most stores are company owned or licensed. In Japan, Russia, and the coast of eastern China, most locations are joint venture. This likely has something to do with how Starbucks expands into markets that are pretty different compared to Western ones.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

Twin_Cities <- get_stamenmap(
    bbox = c(left = -93.7, bottom = 44.6, right = -92.6, top = 45.3), 
    maptype = "terrain",
    zoom = 11)
ggmap(Twin_Cities) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude),
             size = .5) +
  theme_map() +
  labs(title = "Twin Cities Starbucks Locations")

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

It controls the level of detail of the map. The more zoomed in it gets the more streets and names can be seen.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

Twin_Cities <- get_stamenmap(
    bbox = c(left = -93.7, bottom = 44.6, right = -92.6, top = 45.3), 
    maptype = "watercolor",
    zoom = 11)
ggmap(Twin_Cities) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude),
             size = .5) +
  theme_map()+
  labs(title = "Twin Cities Starbucks Locations")

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

Twin_Cities <- get_stamenmap(
    bbox = c(left = -93.7, bottom = 44.6, right = -92.6, top = 45.3), 
    maptype = "terrain",
    zoom = 11)
ggmap(Twin_Cities) +
  geom_point(data = Starbucks,
             aes(x = Longitude, y = Latitude),
             size = .5) +
  annotate(geom = "text", x = -93.169, y = 44.937, label = "Macalester",
           size = 2.5, 
           color = "darkorange2") +
  theme_map()+
  labs(title = "Twin Cities Starbucks Locations")

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>%  
  #Reads Data in and creates new data set for census data
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  #Separates the dot from the state name by moving it to a new column   
  select(-dot) %>% 
  #Selects everything except for the dot column, removing the dot
  mutate(state = str_to_lower(state))
  #Converts the state names to lower case

starbucks_with_2018_pop_est <-
  #Funnels operations into new data set
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  #Joins data sets w/ # of Starbucks per state and population per state in 2018
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)
  #Divides # of Starbucks per state by population in that state to find Starbucks per capita in each state, then multiplies it by 10,000 to find the # of Starbucks per 10,000

6. dplyr review: Look through the code above and describe what each line of code does.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

US_Starbucks <-
Starbucks %>%
  filter(Country == "US") %>%
  filter(!`State/Province` == "AK",
         !`State/Province` == "HI")

states_map <- map_data("state")
starbucks_with_2018_pop_est %>%
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
   geom_point(data = US_Starbucks,
              aes(x = Longitude, y = Latitude),
             size = .5,
             alpha = .5) +
  scale_fill_gradient(low="blue", high="red") +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  labs(title = "States Colored by Starbucks Stores per 10,000 People",
       subtitle = "Created by William Wentworth")

It looks like the West Coast has the most Starbucks per capita, as those states are far more pinkish red than the rest of the country. This is probably because it started in Seattle and would’ve expanded there first. It’s also interesting to see how the Starbucks locations are spread out across the country. It’s probably a good indication of where the urban populations are across the U.S.

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

favorite_places <- tibble(
  Location = c("Frankfort, MI", "Grandparents House", "Greenbush Brewery", "Wrigley Field", "Nana's House", "Starved Rock", "Home", "Thanh Linh Restaurant", "Grandview Drive", "Macalester College"), 
  Longitude = c(-86.245670, -83.549477, -86.59458, -87.655526, -87.98, -89.004, -89.591996, -89.612159,-89.560147, -93.1676),  
  Latitude = c(44.632124, 44.226392, 41.88551, 41.948271, 42.077569, 41.319, 40.690369, 40.699871, 40.742674, 44.93942),
  Top_3 = c("Yes", "No", "No", "No", "No", "No", "Yes", "No", "No", "Yes"))

pal <- colorFactor("Spectral",
                   domain = favorite_places$Top_3)
                    
leaflet(favorite_places) %>% 
  addTiles() %>%
  addCircles(lng = ~Longitude, 
             lat = ~Latitude, 
             label = ~Location, 
             weight = 10, 
             opacity = 1,
             color = ~pal(favorite_places$Top_3)) %>%
  addPolylines(lng = ~Longitude, 
               lat = ~Latitude) %>%
  addLegend("topleft",
            pal = pal,
            values = ~Top_3)

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

DC <- get_stamenmap(
    bbox = c(left = -77.3, bottom = 38.65, right = -76.8, top = 39.2), 
    maptype = "terrain",
    zoom = 10)

Trips %>%
  group_by(sstation) %>%
  mutate(Total_departures = n()) %>%
  left_join(Stations,
            by = c("sstation" = "name")) -> NewTrips 
  
pal2 <- colorNumeric("Spectral",
                   domain = NewTrips$Total_departures)
  
ggmap(DC) +
  geom_point(data = NewTrips,
             aes(x = long, y = lat, color = Total_departures),
             size = .5) +
  scale_color_viridis_c(option = "magma") +
  theme_map()+
  labs(title = "D.C. Bike Rental Stations") 

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

Trips %>%
  group_by(sstation) %>%
  mutate(Total_departures = n()) %>%
  group_by(sstation, client) %>%
  mutate(proportion = n()/Total_departures) %>%
  filter(client == "Casual") %>%
  left_join(Stations,
            by = c("sstation" = "name")) -> Tripsbyclient

ggmap(DC) +
  geom_point(data = Tripsbyclient,
             aes(x = long, y = lat, color = proportion),
             size = .8) +
  scale_color_viridis_c(option = "magma") +
  theme_map() +
  labs(title = "D.C. Bike Rental Stations Use by Casual Riders")

The stations with the highest proportion of casual riders are mostly in the center of DC. It looks like they’re all clustered in or near the National Mall, the most touristy part of the city. Most of the lower proportion stations are located outside of downtown, implying that the registered users are probably commuting with them.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

currentcovid19 <- covid19 %>%
  filter(date == "2020-09-27") %>%
  filter(!state == c("Northern Mariana Islands","Alaska")) %>%
  filter(!state == "Virgin Islands") %>%
  filter(!state == "Guam") %>%
  filter(!state == "Hawaii") %>%
  filter(!state == "Puerto Rico") %>%
  mutate(state_name = str_to_lower(state)) 

currentcovid19 %>%
 ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = cases)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() + 
  labs(title = "Covid Cases by State")

The more populous states are all lighter colors, meaning they have more active cases. The graphs issue is that it doesn’t take into account population so even if states like California have fewer cases per capita than say Alabama, this graph will still show California as having way more cases than Alabama.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid_with_2018_pop_est <-
  currentcovid19 %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(`Cases per 10000 People` = (cases/est_pop_2018)*10000)

covid_with_2018_pop_est %>%
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = `Cases per 10000 People`)) +
  scale_fill_gradient(low="cadetblue1", high="midnightblue") +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() + 
  labs(title = "Covid Cases per 10,000 People by State")

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

comparecovid19 <- covid19 %>%
  filter(date == "2020-09-27" | date == "2020-08-09" | date == "2020-06-14" | date == "2020-04-07") %>%
  filter(!state == c("Northern Mariana Islands","Alaska")) %>%
  filter(!state == "Virgin Islands") %>%
  filter(!state == "Guam") %>%
  filter(!state == "Hawaii") %>%
  filter(!state == "Puerto Rico") %>%
  mutate(state_name = str_to_lower(state))

comparecovid_with_2018_pop_est <-
  comparecovid19 %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(`Cases per 10000 People` = (cases/est_pop_2018)*10000)

comparecovid_with_2018_pop_est %>%
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = `Cases per 10000 People`)) +
  scale_fill_gradient(low="cadetblue1", high="midnightblue") +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() + 
  facet_wrap(vars(date))+ 
  labs(title = "Covid Cases per 10,000 People by State")

The map gets progressively darker as the months go on. There are way more cases now per 10,000 people than there were at the start and even middle of the pandemic.

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

MplsStops %>%
  group_by(neighborhood) %>%
  mutate(total_neighborhood_stops = n()) %>%
  group_by(neighborhood, problem) %>%
  mutate(prop_suspicious = n()/total_neighborhood_stops) %>%
  filter(problem == "suspicious") %>%
  group_by(neighborhood, problem, total_neighborhood_stops, prop_suspicious) %>% 
  distinct(neighborhood) -> mpls_suspicious

mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

pal3 <- colorFactor("Spectral",
                   domain = MplsStops$problem)
                    
leaflet(MplsStops) %>% 
  addTiles() %>%
  addCircleMarkers(lng = ~long, 
             lat = ~lat,
             radius = 3,
             color = ~pal3(MplsStops$problem),
             stroke = FALSE) %>%
  addLegend("topleft",
            pal = pal3,
            values = ~problem)

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods-20201001", quiet = TRUE)

mpls_nbhd %>%
  left_join(MplsDemo,
            by = c("BDNAME" = "neighborhood")) %>%
  left_join(mpls_suspicious,
            by = c("BDNAME" = "neighborhood")) -> mpls_all

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

pal4 <- colorNumeric("Spectral",
                     domain = mpls_all$prop_suspicious)

leaflet(mpls_all) %>% 
  addTiles() %>%
  addPolygons(fillColor = ~pal4(mpls_all$prop_suspicious),
              fillOpacity = 0.7,
              label = ~BDNAME) %>%
  addLegend(pal = pal4, 
            values = ~prop_suspicious, 
            opacity = 0.5,
            position = "bottomright")

According to the map, it looks like neighborhoods on the southeast side of the city have higher rates of suspicious stops. The northeastern side has much smaller rates of suspicious stops, making the east side almost perfectly divided by the river. The west side of the city is less divided and is mostly yellowish/orange, meaning its rates of suspicous stops are around and below 50%

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

MplsStops %>%
  left_join(MplsDemo,
            by = "neighborhood") %>%
  group_by(neighborhood) %>%
  mutate(total_neighborhood_stops = n(),
         proportional_stops = total_neighborhood_stops/population) %>%
  group_by(neighborhood, problem, total_neighborhood_stops, proportional_stops) %>% 
  distinct(neighborhood) ->  Mpls_combined 

mpls_nbhd %>%
  left_join(Mpls_combined,
            by = c("BDNAME" = "neighborhood")) -> Mpls_combined_vis

pal5 <- colorNumeric("Spectral",
                     domain = Mpls_combined_vis$proportional_stops)

leaflet(Mpls_combined_vis) %>% 
  addTiles() %>%
  addPolygons(fillColor = ~pal5(Mpls_combined_vis$proportional_stops),
              fillOpacity = 0.7) %>%
  addLegend(pal = pal5, 
            values = ~proportional_stops, 
            opacity = 0.5,
            position = "bottomright")

I wanted to see how the stops in each neighborhood compared to their population. The graph shows stops proportional to their population. For example, the neighborhood with the dark purple color has a high number of stops in proportion to their population. This could be indicative of over-policing of certain neighborhoods but could also just come down amount of traffic that each neighborhood has on average.

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

Here

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?